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Volume 8, Issue 5 (Suppl)
J Biom Biostat, an open access journal
ISSN: 2155-6180
Biostatisitcs 2017
November 13-14, 2017
November 13-14, 2017 | Atlanta, USA
6
th
International Conference on
Biostatistics and Bioinformatics
Big data analysis in bioinformatics
W
ith the increasing use of advanced technology and the exploding amount of data in bioinformatics, it is imperative to
introduce effective and efficient methods to handle Big data using the distributed and parallel computing technologies.
Big data analytics can examine large data sets, analyze and correlate genomic and proteomic information. In this presentation,
we begin with an overview of Big data and Big data analytics, we then address several challenging and important tasks in
bioinformatics such as analyzing coding, noncoding regions and finding similarities for coding and noncoding regions as
well as many other issues. We further study mutual information-based gene or feature selection method where features
are wavelet-based; the bootstrap techniques employed to obtain an accurate estimate of the mutual information and other
new methods to analyze data. Given the multi-scale structure of most biological data, several methods will be presented to
achieve improvements in the quality of mathematical or statistical analysis of such data. In a DNA strand, it is essential to find
sequences, which can be transcribed to complementary parts of the DNA strand. We will mention several methods to identify
protein coding regions. We also use some special variance and entropy to analyze similarities among coding and noncoding
regions of several DNA sequences respectively and compare the resulting data. We will address the use of big data analytics in
many phases of the bioinformatics analysis pipeline.
Biography
En-Bing Lin is a Professor of Mathematics at Central Michigan University, USA. He has been associated with several institutions including Massachusetts Institute
of Technology, University of Wisconsin-Milwaukee, University of California, Riverside, University of Toledo, UCLA, and University of Illinois at Chicago. He has
received his PhD from Johns Hopkins University. His research interests include Data Analysis, Applied and Computational Mathematics, and Mathematical Physics.
He has Supervised a number of graduate and undergraduate students. He serves on the Editorial Boards of several journals. He has organized several special
sessions at regional IEEE conferences and many other professional meetings.
lin1e@cmich.eduEn-Bing Lin
Central Michigan University, USA
En-Bing Lin, J Biom Biostat 2017, 8:5 (Suppl)
DOI: 10.4172/2155-6180-C1-004